Extracting multi-modal dynamics of objects using RNNPB

Tetsuya Ogata*, Hayato Ohba, Jun Tani, Kazunori Komatani, Hiroshi G. Okuno

*この研究の対応する著者

研究成果

18 被引用数 (Scopus)

抄録

Dynamic features play an important role in recognizing objects that have similar static features in colors and or shapes. This paper focuses on active sensing that exploits dynamic feature of an object. An extended version of the robot, Robovie-IIs, moves an object by its arm to obtain its dynamic features. Its issue is how to extract symbols from various kinds of temporal states of the object. We use the recurrent neural network with parametric bias (RNNPB) that generates selforganized nodes in the parametric bias space. The RNNPB with 42 neurons was trained with the data of sounds, trajectories, and tactile sensors generated while the robot was moving/hitting an object with its own arm. The clusters of 20 kinds of objects were successfully self-organized. The experiments with unknown (not trained) objects demonstrated that our method configured them in the PB space appropriately, which proves its generalization capability.

本文言語English
ホスト出版物のタイトル2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS
ページ160-165
ページ数6
DOI
出版ステータスPublished - 2005 12 1
外部発表はい
イベントIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005 - Edmonton, AB, Canada
継続期間: 2005 8 22005 8 6

出版物シリーズ

名前2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Conference

ConferenceIEEE IRS/RSJ International Conference on Intelligent Robots and Systems, IROS 2005
国/地域Canada
CityEdmonton, AB
Period05/8/205/8/6

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用
  • 制御およびシステム工学

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